Traffic Sign Recognition by Image Preprocessing and Deep Learning

Due to the improvement in the car manifacture, the rate of road traffic accidents is increasing. To solve these problems, there is loads of attention in research on the development of driver assistance systems, where the main innovation is traffic sign recognition (TSR). In this article, a special convolutional neural network model with high accuracy compared to traditional models is used for TSR. The Uzbek Traffic Sign Dataset (UTSD) applied in the zone of Uzbekistan was created, consisting of 21.923 images belonging to 56 classes. We proposed a parallel computing method for real-time processing of video haze removal. Our utilization can process the 1920 × 1080 video series with 176 frames per second for the dark channel prior (DCP) algorithm. 8.94 times reduction of calculation time compared to the Central Processing Unit (CPU) was achieved by performing the TSR process on the Graphics Processing Unit (GPU). The algorithms used to detect traffic signs are improved YOLOv5. The results showed a 3.9% increase in accuracy.